A PSF estimation method based on joint transform correlation

2016 ◽  
Author(s):  
Tao Zhang ◽  
Hongli Wang ◽  
Jinhui Lu
Keyword(s):  
Estimation Method ◽  
Joint Transform Correlation ◽  
Psf Estimation

scholarly journals Optimal PSF Estimation for Simple Optical System Using a Wide-Band Sensor Based on PSF Measurement

Sensors ◽  
2018 ◽  
Vol 18 (10) ◽  
pp. 3552 ◽  
Author(s):  
Yunda Zheng ◽  
Wei Huang ◽  
Yun Pan ◽  
Mingfei Xu
Keyword(s):  
Image Quality ◽  
Optical System ◽  
Estimation Method ◽  
Wide Band ◽  
Optical Systems ◽  
Blind Estimation ◽  
Image Deconvolution ◽  
Color Channel ◽  
Spread Function ◽  
Psf Estimation

Simple optical system imaging is a method to simplify optical systems by removing aberrations using image deconvolution. The point spread function (PSF) used in deconvolution is an important factor that affects the image quality. However, it is difficult to obtain optimal PSFs. The blind estimation of PSFs relies heavily on the information in the image. Measured PSFs are often misused because real sensors are wide-band. We present an optimal PSF estimation method based on PSF measurements. Narrow-band PSF measurements at a single depth are used to calibrate the optical system; these enable the simulation of real lenses. Then, we simulate PSFs in the wavelength pass range of each color channel all over the field. The optimal PSFs are computed according to these simulated PSFs. The results indicated that the use of the optimal PSFs significantly reduces the artifacts caused by misuse of PSFs, and enhances the image quality.

PSF Estimation Method of Simple-lens Camera Using Normal Sinh-Arcsinh Model Based on Noise Image Pairs

IEEE Access ◽  
2021 ◽  
pp. 1-1
Author(s):  
Dazhi Zhan ◽  
Weili Li ◽  
Xiaoqing Yin ◽  
Caiyun Niu ◽  
Jin Liu
Keyword(s):  
Estimation Method ◽  
Model Based ◽  
Noise Image ◽  
Image Pairs ◽  
Psf Estimation

Coaches estimation method for the athletes (CEM)

1995 ◽  
Author(s):  
Nagykaldi Csaba ◽  
Manohar Singh Badhan
Keyword(s):  
Estimation Method

Theoretical, methodological and practical issues of choice models

2018 ◽  
Vol 1 (1) ◽  
pp. 21-37
Author(s):  
Bharat P. Bhatta
Keyword(s):  
Discrete Choice ◽  
Model Building ◽  
Estimation Method ◽  
Choice Models ◽  
Discrete Choice Models ◽  
Decision Makers ◽  
Model Formulation ◽  
Random Utility Maximization ◽  
The Relationship ◽  
Method Model

This paper analyzes and synthesizes the fundamentals of discrete choice models. This paper alsodiscusses the basic concept and theory underlying the econometrics of discrete choice, specific choicemodels, estimation method, model building and tests, and applications of discrete choice models. Thiswork highlights the relationship between economic theory and discrete choice models: how economictheory contributes to choice modeling and vice versa. Keywords: Discrete choice models; Random utility maximization; Decision makers; Utility function;Model formulation

An Absolute Phase Estimation Method for Interferometry Imagery

2019 ◽  
Vol 1 (2) ◽  
pp. 14-19
Author(s):  
Sui Ping Lee ◽  
Yee Kit Chan ◽  
Tien Sze Lim
Keyword(s):  
Noise Suppression ◽  
Estimation Method ◽  
Phase Image ◽  
Phase Estimation ◽  
Phase Reconstruction ◽  
Estimation Algorithms ◽  
Absolute Phase ◽  
Filtering Rate ◽  
Interferometric Phase ◽  
Image Performance

Accurate interpretation of interferometric image requires an extremely challenging task based on actual phase reconstruction for incomplete noise observation. In spite of the establishment of comprehensive solutions, until now, a guaranteed means of solution method is yet to exist. The initially observed interferometric image is formed by 2π-periodic phase image that wrapped within (-π, π]. Such inverse problem is further corrupted by noise distortion and leads to the degradation of interferometric image. In order to overcome this, an effective algorithm that enables noise suppression and absolute phase reconstruction of interferometric phase image is proposed. The proposed method incorporates an improved order statistical filter that is able to adjust or vary on its filtering rate by adapting to phase noise level of relevant interferometric image. Performance of proposed method is evaluated and compared with other existing phase estimation algorithms. The comparison is based on a series of computer simulated and real interferometric data images. The experiment results illustrate the effectiveness and competency of the proposed method.

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